There are many types of networks ranging from simple networks(Perceptrons) to complex self -orgnising networks(Kohonen networks).Similarly, there are many different kinds of learning rules used by neural networks, the common being the delta rule. The delta rule is often utilized by the most common class of ANNs called 'backpropagational neural networks' (BPNNs). Backpropagation is an abbreviation for the backwards propagation of error.
With the delta rule, as with other types of backpropagation, 'learning' is a supervised process that occurs with each cycle or 'epoch' (i.e. each time the network is presented with a new input pattern) through a forward activation flow of outputs, and the backwards error propagation of weight adjustments. More simply, when a neural network is initially presented with a pattern it makes a random 'guess' as to what it might be. It then sees how far its answer was from the actual one and makes an appropriate adjustment to its connection weights.The other learning rule that is mostly used is feed-forward type of neural network.

Comparsion with other Prediction methods-:
For the sake of comparison we had implemented two methods of T cell epitope prediction AMPHI and EpiMer on our dataset. Unfortunately prediction accuracy of AMPHI method is 53% at a cutoff score where sensitivity and specificity are nearly equal, which is not better than random prediction. The EpiMer was able to classify the data with 62% of accuracy. It clearly demonstrated that our prediction methods based on the SVM and ANN is better in CTL epitope prediction as compared previously developed method.